Overview

Dataset statistics

Number of variables13
Number of observations1564
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory171.1 KiB
Average record size in memory112.0 B

Variable types

Categorical4
Numeric9

Alerts

PropertyGFABuilding(s) is highly overall correlated with SiteEnergyUse(kBtu) and 1 other fieldsHigh correlation
SiteEUI(kBtu/sf) is highly overall correlated with SiteEnergyUse(kBtu) and 1 other fieldsHigh correlation
SiteEnergyUse(kBtu) is highly overall correlated with PropertyGFABuilding(s) and 2 other fieldsHigh correlation
TotalGHGEmissions is highly overall correlated with PropertyGFABuilding(s) and 2 other fieldsHigh correlation
Use_Steam is highly imbalanced (65.0%)Imbalance
SiteEnergyUse(kBtu) has unique valuesUnique
NumberofBuildings has 52 (3.3%) zerosZeros
PropertyGFAParking has 1238 (79.2%) zerosZeros

Reproduction

Analysis started2025-12-19 15:43:24.514364
Analysis finished2025-12-19 15:43:34.272904
Duration9.76 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Distinct20
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
Small- and Mid-Sized Office
286 
Other
244 
Warehouse
184 
Large Office
156 
Mixed Use Property
128 
Other values (15)
566 

Length

Max length27
Median length21
Mean length14.721867
Min length5

Characters and Unicode

Total characters23025
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowOther

Common Values

ValueCountFrequency (%)
Small- and Mid-Sized Office286
18.3%
Other244
15.6%
Warehouse184
11.8%
Large Office156
10.0%
Mixed Use Property128
8.2%
Retail Store85
 
5.4%
Hotel74
 
4.7%
Worship Facility69
 
4.4%
Distribution Center53
 
3.4%
K-12 School50
 
3.2%
Other values (10)235
15.0%

Length

2025-12-19T16:43:34.343336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
office480
14.1%
small286
 
8.4%
and286
 
8.4%
mid-sized286
 
8.4%
other244
 
7.2%
warehouse196
 
5.8%
large156
 
4.6%
mixed128
 
3.8%
use128
 
3.8%
property128
 
3.8%
Other values (24)1082
31.8%

Most occurring characters

ValueCountFrequency (%)
e2774
 
12.0%
i1899
 
8.2%
1836
 
8.0%
r1507
 
6.5%
a1340
 
5.8%
t1098
 
4.8%
d1059
 
4.6%
f1000
 
4.3%
l995
 
4.3%
o926
 
4.0%
Other values (32)8591
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)23025
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2774
 
12.0%
i1899
 
8.2%
1836
 
8.0%
r1507
 
6.5%
a1340
 
5.8%
t1098
 
4.8%
d1059
 
4.6%
f1000
 
4.3%
l995
 
4.3%
o926
 
4.0%
Other values (32)8591
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)23025
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2774
 
12.0%
i1899
 
8.2%
1836
 
8.0%
r1507
 
6.5%
a1340
 
5.8%
t1098
 
4.8%
d1059
 
4.6%
f1000
 
4.3%
l995
 
4.3%
o926
 
4.0%
Other values (32)8591
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)23025
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2774
 
12.0%
i1899
 
8.2%
1836
 
8.0%
r1507
 
6.5%
a1340
 
5.8%
t1098
 
4.8%
d1059
 
4.6%
f1000
 
4.3%
l995
 
4.3%
o926
 
4.0%
Other values (32)8591
37.3%

Neighborhood
Categorical

Distinct13
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
DOWNTOWN
342 
GREATER DUWAMISH
324 
MAGNOLIA / QUEEN ANNE
154 
LAKE UNION
140 
NORTHEAST
120 
Other values (8)
484 

Length

Max length21
Median length10
Mean length10.799872
Min length4

Characters and Unicode

Total characters16891
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDOWNTOWN
2nd rowDOWNTOWN
3rd rowDOWNTOWN
4th rowDOWNTOWN
5th rowDOWNTOWN

Common Values

ValueCountFrequency (%)
DOWNTOWN342
21.9%
GREATER DUWAMISH324
20.7%
MAGNOLIA / QUEEN ANNE154
9.8%
LAKE UNION140
9.0%
NORTHEAST120
 
7.7%
EAST113
 
7.2%
NORTHWEST83
 
5.3%
NORTH67
 
4.3%
BALLARD63
 
4.0%
CENTRAL49
 
3.1%
Other values (3)109
 
7.0%

Length

2025-12-19T16:43:34.455464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
downtown342
13.7%
greater324
13.0%
duwamish324
13.0%
magnolia154
 
6.2%
154
 
6.2%
queen154
 
6.2%
anne154
 
6.2%
lake140
 
5.6%
union140
 
5.6%
northeast120
 
4.8%
Other values (8)484
19.4%

Most occurring characters

ValueCountFrequency (%)
N1899
11.2%
E1766
10.5%
A1690
10.0%
T1435
 
8.5%
O1315
 
7.8%
W1126
 
6.7%
R1072
 
6.3%
926
 
5.5%
D813
 
4.8%
S774
 
4.6%
Other values (11)4075
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)16891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N1899
11.2%
E1766
10.5%
A1690
10.0%
T1435
 
8.5%
O1315
 
7.8%
W1126
 
6.7%
R1072
 
6.3%
926
 
5.5%
D813
 
4.8%
S774
 
4.6%
Other values (11)4075
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N1899
11.2%
E1766
10.5%
A1690
10.0%
T1435
 
8.5%
O1315
 
7.8%
W1126
 
6.7%
R1072
 
6.3%
926
 
5.5%
D813
 
4.8%
S774
 
4.6%
Other values (11)4075
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N1899
11.2%
E1766
10.5%
A1690
10.0%
T1435
 
8.5%
O1315
 
7.8%
W1126
 
6.7%
R1072
 
6.3%
926
 
5.5%
D813
 
4.8%
S774
 
4.6%
Other values (11)4075
24.1%

YearBuilt
Real number (ℝ)

Distinct113
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1962.1208
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2025-12-19T16:43:34.575479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1906
Q11930
median1966
Q31990
95-th percentile2009
Maximum2015
Range115
Interquartile range (IQR)60

Descriptive statistics

Standard deviation33.114549
Coefficient of variation (CV)0.016876916
Kurtosis-1.0625351
Mean1962.1208
Median Absolute Deviation (MAD)27
Skewness-0.28352383
Sum3068757
Variance1096.5734
MonotonicityNot monotonic
2025-12-19T16:43:34.718585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190042
 
2.7%
197031
 
2.0%
200029
 
1.9%
196029
 
1.9%
191028
 
1.8%
200828
 
1.8%
196228
 
1.8%
197928
 
1.8%
192627
 
1.7%
196925
 
1.6%
Other values (103)1269
81.1%
ValueCountFrequency (%)
190042
2.7%
19013
 
0.2%
19028
 
0.5%
19031
 
0.1%
190412
 
0.8%
19054
 
0.3%
190612
 
0.8%
190712
 
0.8%
190811
 
0.7%
190914
 
0.9%
ValueCountFrequency (%)
201510
 
0.6%
201414
0.9%
201315
1.0%
201210
 
0.6%
20115
 
0.3%
201010
 
0.6%
200922
1.4%
200828
1.8%
200711
 
0.7%
200617
1.1%

NumberofBuildings
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0837596
Minimum0
Maximum27
Zeros52
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2025-12-19T16:43:34.826751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.10661
Coefficient of variation (CV)1.0210844
Kurtosis311.19415
Mean1.0837596
Median Absolute Deviation (MAD)0
Skewness15.712179
Sum1695
Variance1.2245857
MonotonicityNot monotonic
2025-12-19T16:43:34.920578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
11466
93.7%
052
 
3.3%
214
 
0.9%
311
 
0.7%
57
 
0.4%
45
 
0.3%
62
 
0.1%
82
 
0.1%
271
 
0.1%
111
 
0.1%
Other values (3)3
 
0.2%
ValueCountFrequency (%)
052
 
3.3%
11466
93.7%
214
 
0.9%
311
 
0.7%
45
 
0.3%
57
 
0.4%
62
 
0.1%
82
 
0.1%
101
 
0.1%
111
 
0.1%
ValueCountFrequency (%)
271
 
0.1%
231
 
0.1%
141
 
0.1%
111
 
0.1%
101
 
0.1%
82
 
0.1%
62
 
0.1%
57
0.4%
45
0.3%
311
0.7%

NumberofFloors
Real number (ℝ)

Distinct38
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9482097
Minimum0
Maximum99
Zeros14
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2025-12-19T16:43:35.033212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile12
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.4448203
Coefficient of variation (CV)1.3790606
Kurtosis73.167805
Mean3.9482097
Median Absolute Deviation (MAD)1
Skewness6.3815287
Sum6175
Variance29.646068
MonotonicityNot monotonic
2025-12-19T16:43:35.152676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1423
27.0%
2349
22.3%
3251
16.0%
4157
 
10.0%
5107
 
6.8%
678
 
5.0%
739
 
2.5%
1019
 
1.2%
819
 
1.2%
1118
 
1.2%
Other values (28)104
 
6.6%
ValueCountFrequency (%)
014
 
0.9%
1423
27.0%
2349
22.3%
3251
16.0%
4157
 
10.0%
5107
 
6.8%
678
 
5.0%
739
 
2.5%
819
 
1.2%
98
 
0.5%
ValueCountFrequency (%)
991
 
0.1%
491
 
0.1%
422
0.1%
411
 
0.1%
391
 
0.1%
371
 
0.1%
362
0.1%
341
 
0.1%
332
0.1%
293
0.2%

PropertyGFAParking
Real number (ℝ)

Zeros 

Distinct320
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12171.737
Minimum0
Maximum407795
Zeros1238
Zeros (%)79.2%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2025-12-19T16:43:35.278781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile86218.85
Maximum407795
Range407795
Interquartile range (IQR)0

Descriptive statistics

Standard deviation37513.829
Coefficient of variation (CV)3.0820439
Kurtosis28.079226
Mean12171.737
Median Absolute Deviation (MAD)0
Skewness4.6878805
Sum19036597
Variance1.4072873 × 109
MonotonicityNot monotonic
2025-12-19T16:43:35.461732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01238
79.2%
133203
 
0.2%
1001762
 
0.1%
258002
 
0.1%
108002
 
0.1%
300002
 
0.1%
204162
 
0.1%
792841
 
0.1%
555041
 
0.1%
38341
 
0.1%
Other values (310)310
 
19.8%
ValueCountFrequency (%)
01238
79.2%
12631
 
0.1%
13921
 
0.1%
22111
 
0.1%
23521
 
0.1%
37641
 
0.1%
38341
 
0.1%
42221
 
0.1%
42561
 
0.1%
49141
 
0.1%
ValueCountFrequency (%)
4077951
0.1%
3689801
0.1%
3351091
0.1%
3037071
0.1%
2729001
0.1%
2392521
0.1%
2286681
0.1%
2065971
0.1%
2065801
0.1%
2059701
0.1%

PropertyGFABuilding(s)
Real number (ℝ)

High correlation 

Distinct1484
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86909.331
Minimum3636
Maximum1172127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2025-12-19T16:43:35.604912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3636
5-th percentile20882.55
Q127926.75
median45721.5
Q392849
95-th percentile315736.9
Maximum1172127
Range1168491
Interquartile range (IQR)64922.25

Descriptive statistics

Standard deviation118219.13
Coefficient of variation (CV)1.3602582
Kurtosis21.231987
Mean86909.331
Median Absolute Deviation (MAD)21380.5
Skewness3.9991489
Sum1.3592619 × 108
Variance1.3975762 × 1010
MonotonicityNot monotonic
2025-12-19T16:43:35.745151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360007
 
0.4%
288007
 
0.4%
259207
 
0.4%
216006
 
0.4%
240005
 
0.3%
333003
 
0.2%
450003
 
0.2%
200002
 
0.1%
336002
 
0.1%
219002
 
0.1%
Other values (1474)1520
97.2%
ValueCountFrequency (%)
36361
0.1%
109251
0.1%
112851
0.1%
114401
0.1%
116851
0.1%
119681
0.1%
127691
0.1%
128061
0.1%
131571
0.1%
141011
0.1%
ValueCountFrequency (%)
11721271
0.1%
10479341
0.1%
10048131
0.1%
9624281
0.1%
9342921
0.1%
8880491
0.1%
8617021
0.1%
7945921
0.1%
7913961
0.1%
7544551
0.1%

SiteEUI(kBtu/sf)
Real number (ℝ)

High correlation 

Distinct925
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.2711
Minimum1.4
Maximum834.40002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2025-12-19T16:43:36.144622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile13.1
Q134.675
median53.099998
Q382.65
95-th percentile210.08501
Maximum834.40002
Range833.00002
Interquartile range (IQR)47.974999

Descriptive statistics

Standard deviation69.691825
Coefficient of variation (CV)0.96431112
Kurtosis22.958621
Mean72.2711
Median Absolute Deviation (MAD)22.099998
Skewness3.6919308
Sum113032
Variance4856.9505
MonotonicityNot monotonic
2025-12-19T16:43:36.279489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.700000768
 
0.5%
33.700000766
 
0.4%
38.400001536
 
0.4%
56.599998476
 
0.4%
52.299999246
 
0.4%
356
 
0.4%
30.899999626
 
0.4%
52.200000766
 
0.4%
325
 
0.3%
435
 
0.3%
Other values (915)1504
96.2%
ValueCountFrequency (%)
1.3999999761
0.1%
2.0999999051
0.1%
2.2999999521
0.1%
31
0.1%
3.2000000481
0.1%
3.52
0.1%
3.5999999052
0.1%
3.7999999521
0.1%
4.3000001911
0.1%
4.4000000951
0.1%
ValueCountFrequency (%)
834.40002441
0.1%
696.70001221
0.1%
694.70001221
0.1%
593.59997561
0.1%
465.51
0.1%
456.60000611
0.1%
438.20001221
0.1%
412.70001221
0.1%
404.10000611
0.1%
400.79998781
0.1%

SiteEnergyUse(kBtu)
Real number (ℝ)

High correlation  Unique 

Distinct1564
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6113664.9
Minimum57133.199
Maximum98960776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2025-12-19T16:43:36.415245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum57133.199
5-th percentile439655.81
Q11243815.4
median2654851.8
Q36783593.8
95-th percentile24193820
Maximum98960776
Range98903643
Interquartile range (IQR)5539778.3

Descriptive statistics

Standard deviation9303900
Coefficient of variation (CV)1.5218204
Kurtosis19.506558
Mean6113664.9
Median Absolute Deviation (MAD)1794109.2
Skewness3.6977379
Sum9.5617719 × 109
Variance8.6562555 × 1013
MonotonicityNot monotonic
2025-12-19T16:43:36.557911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7226362.51
 
0.1%
83879331
 
0.1%
67945841
 
0.1%
141726061
 
0.1%
120866161
 
0.1%
57587951
 
0.1%
6298131.51
 
0.1%
137238201
 
0.1%
160166441
 
0.1%
269411101
 
0.1%
Other values (1554)1554
99.4%
ValueCountFrequency (%)
57133.199221
0.1%
79711.796881
0.1%
90558.703131
0.1%
97690.398441
0.1%
1069181
0.1%
111969.70311
0.1%
1131301
0.1%
116486.60161
0.1%
117438.39841
0.1%
123767.20311
0.1%
ValueCountFrequency (%)
989607761
0.1%
906096401
0.1%
680907281
0.1%
653369801
0.1%
650472841
0.1%
591076201
0.1%
587613041
0.1%
577644081
0.1%
564852041
0.1%
531661561
0.1%

TotalGHGEmissions
Real number (ℝ)

High correlation 

Distinct1502
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.09876
Minimum0
Maximum3768.66
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2025-12-19T16:43:36.694663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2365
Q120.4275
median49.285
Q3133.945
95-th percentile502.868
Maximum3768.66
Range3768.66
Interquartile range (IQR)113.5175

Descriptive statistics

Standard deviation269.17861
Coefficient of variation (CV)2.0073162
Kurtosis53.326446
Mean134.09876
Median Absolute Deviation (MAD)38.135
Skewness6.0912092
Sum209730.46
Variance72457.125
MonotonicityNot monotonic
2025-12-19T16:43:36.831536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.33
 
0.2%
25.142
 
0.1%
32.222
 
0.1%
3.252
 
0.1%
29.182
 
0.1%
53.252
 
0.1%
10.052
 
0.1%
49.582
 
0.1%
39.052
 
0.1%
2.822
 
0.1%
Other values (1492)1543
98.7%
ValueCountFrequency (%)
01
0.1%
0.41
0.1%
0.631
0.1%
0.681
0.1%
0.751
0.1%
0.791
0.1%
0.811
0.1%
0.821
0.1%
0.861
0.1%
0.871
0.1%
ValueCountFrequency (%)
3768.661
0.1%
3278.111
0.1%
2573.751
0.1%
2549.471
0.1%
2489.781
0.1%
2055.821
0.1%
1990.51
0.1%
1789.691
0.1%
1727.111
0.1%
1699.451
0.1%

Use_Steam
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
0
1461 
1
 
103

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1564
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01461
93.4%
1103
 
6.6%

Length

2025-12-19T16:43:36.959893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-19T16:43:37.029552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01461
93.4%
1103
 
6.6%

Most occurring characters

ValueCountFrequency (%)
01461
93.4%
1103
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01461
93.4%
1103
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01461
93.4%
1103
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01461
93.4%
1103
 
6.6%

Use_Gas
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
1
1121 
0
443 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1564
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11121
71.7%
0443
 
28.3%

Length

2025-12-19T16:43:37.111817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-19T16:43:37.186816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11121
71.7%
0443
 
28.3%

Most occurring characters

ValueCountFrequency (%)
11121
71.7%
0443
 
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11121
71.7%
0443
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11121
71.7%
0443
 
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11121
71.7%
0443
 
28.3%

DistanceToCenter
Real number (ℝ)

Distinct1501
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.480797
Minimum0.072841784
Maximum14.258402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2025-12-19T16:43:37.276602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.072841784
5-th percentile0.46473594
Q11.4000644
median3.5614403
Q36.7378663
95-th percentile11.466463
Maximum14.258402
Range14.185561
Interquartile range (IQR)5.3378019

Descriptive statistics

Standard deviation3.5390358
Coefficient of variation (CV)0.78982283
Kurtosis-0.30065179
Mean4.480797
Median Absolute Deviation (MAD)2.5299351
Skewness0.77751312
Sum7007.9665
Variance12.524774
MonotonicityNot monotonic
2025-12-19T16:43:37.411202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7381505818
 
0.5%
2.4128351096
 
0.4%
8.9853054085
 
0.3%
9.6596985844
 
0.3%
2.6611673214
 
0.3%
8.3047467193
 
0.2%
5.0328726353
 
0.2%
7.729394493
 
0.2%
0.25181555533
 
0.2%
3.8200135513
 
0.2%
Other values (1491)1522
97.3%
ValueCountFrequency (%)
0.07284178361
0.1%
0.097303079841
0.1%
0.10663811541
0.1%
0.1137525741
0.1%
0.12010974711
0.1%
0.12299400181
0.1%
0.13371281061
0.1%
0.13383182941
0.1%
0.1347332441
0.1%
0.14627623211
0.1%
ValueCountFrequency (%)
14.258402331
0.1%
14.257459321
0.1%
14.224757551
0.1%
14.078612881
0.1%
13.991543231
0.1%
13.976253031
0.1%
13.944269692
0.1%
13.910222091
0.1%
13.756855491
0.1%
13.727681251
0.1%

Interactions

2025-12-19T16:43:33.067048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:25.039437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:26.038019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:26.941509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:27.822973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:29.321836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:30.297167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:31.224318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:32.115644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:33.170530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:25.189382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:26.139998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:27.040408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:27.933723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:29.430474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:30.442152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:31.324876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:32.217769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:33.271484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:25.312923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:26.232081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:27.135024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:28.039683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:29.535868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:30.544346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:31.420198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:32.321536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:33.371742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:25.413171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:26.331852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:27.224839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:28.151946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:29.640351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:30.634932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:31.516308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:32.423350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:33.486188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:25.521618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:26.439481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:27.329581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:28.267012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:29.755072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:30.738121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:31.621458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:32.537781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:33.596780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:25.624748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:26.541064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:27.428709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:28.382063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:29.866594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:30.838465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:31.724836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:32.644285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:33.690941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:25.725227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:26.638245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:27.521676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:28.485345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:29.965964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:30.922416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:31.818191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:32.744735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:33.792439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:25.827559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:26.736358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:27.622560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:28.595594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:30.070257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:31.020773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:31.913476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:32.849908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:33.898460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:25.934355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:26.839634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:27.722164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:28.710255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:30.183779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:31.125463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:32.015449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-19T16:43:32.958399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-19T16:43:37.518970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
DistanceToCenterNeighborhoodNumberofBuildingsNumberofFloorsPrimaryPropertyTypePropertyGFABuilding(s)PropertyGFAParkingSiteEUI(kBtu/sf)SiteEnergyUse(kBtu)TotalGHGEmissionsUse_GasUse_SteamYearBuilt
DistanceToCenter1.0000.4740.087-0.4800.195-0.261-0.202-0.076-0.227-0.1480.1580.3880.237
Neighborhood0.4741.0000.0500.1750.2490.0650.0850.0880.0810.0460.1440.3590.175
NumberofBuildings0.0870.0501.000-0.0470.1410.036-0.018-0.0010.0320.0410.0000.0000.032
NumberofFloors-0.4800.175-0.0471.0000.2240.4790.4330.1360.4210.2730.0690.3160.100
PrimaryPropertyType0.1950.2490.1410.2241.0000.1820.1530.2570.2630.2500.3090.2160.173
PropertyGFABuilding(s)-0.2610.0650.0360.4790.1821.0000.2880.1120.7090.5470.0000.2410.229
PropertyGFAParking-0.2020.085-0.0180.4330.1530.2881.0000.1740.3480.1890.0290.0000.366
SiteEUI(kBtu/sf)-0.0760.088-0.0010.1360.2570.1120.1741.0000.7110.6680.1250.0760.155
SiteEnergyUse(kBtu)-0.2270.0810.0320.4210.2630.7090.3480.7111.0000.8560.0440.2280.273
TotalGHGEmissions-0.1480.0460.0410.2730.2500.5470.1890.6680.8561.0000.1080.2810.156
Use_Gas0.1580.1440.0000.0690.3090.0000.0290.1250.0440.1081.0000.1020.148
Use_Steam0.3880.3590.0000.3160.2160.2410.0000.0760.2280.2810.1021.0000.177
YearBuilt0.2370.1750.0320.1000.1730.2290.3660.1550.2730.1560.1480.1771.000

Missing values

2025-12-19T16:43:34.056457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-19T16:43:34.190632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PrimaryPropertyTypeNeighborhoodYearBuiltNumberofBuildingsNumberofFloorsPropertyGFAParkingPropertyGFABuilding(s)SiteEUI(kBtu/sf)SiteEnergyUse(kBtu)TotalGHGEmissionsUse_SteamUse_GasDistanceToCenter
0HotelDOWNTOWN19271.01208843481.6999977226362.5249.98110.800964
1HotelDOWNTOWN19961.011150648850294.8000038387933.0295.86010.787963
3HotelDOWNTOWN19261.010061320110.8000036794584.0286.43110.945211
4HotelDOWNTOWN19801.01862000113580114.80000314172606.0505.01011.049298
5OtherDOWNTOWN19991.023719860090136.10000612086616.0301.81011.165854
6HotelDOWNTOWN19261.01108300870.8000035758795.0176.14010.858913
7OtherDOWNTOWN19261.08010276161.2999996298131.5221.51110.788990
8HotelDOWNTOWN19041.015016398483.69999713723820.0392.16010.365077
10HotelDOWNTOWN19691.01119279133884119.59999816016644.0691.26110.174395
11HotelDOWNTOWN19981.0256116127201580.00000026941110.0740.97110.133832
PrimaryPropertyTypeNeighborhoodYearBuiltNumberofBuildingsNumberofFloorsPropertyGFAParkingPropertyGFABuilding(s)SiteEUI(kBtu/sf)SiteEnergyUse(kBtu)TotalGHGEmissionsUse_SteamUse_GasDistanceToCenter
3363OtherNORTH19491.0101128557.2000016.456654e+0514.370113.071325
3364OtherBALLARD19111.0101679555.7999999.366165e+0524.73018.695702
3365OtherBALLARD19721.01012769400.7999885.117308e+06216.18018.584330
3367OtherEAST19121.01023445254.8999945.976246e+06259.22013.152211
3368Mixed Use PropertyCENTRAL19941.0102005090.4000021.813404e+0660.81012.247010
3370OtherDELRIDGE19821.0101826151.0000009.320821e+0520.33017.956019
3372OtherDOWNTOWN20041.0101600059.4000029.502762e+0532.17011.308024
3373OtherMAGNOLIA / QUEEN ANNE19741.01013157438.2000125.765898e+06223.54013.881280
3374Mixed Use PropertyGREATER DUWAMISH19891.0101410151.0000007.194712e+0522.11018.689294
3375Mixed Use PropertyGREATER DUWAMISH19381.0101825863.0999981.152896e+0641.27017.932029